When you think of man vs. machine, the first thing that pops into your head might be IBM supercomputer Deep Blue's 1997 victory over world chess champion Garry Kasparov.

Yet many forget that Deep Blue had a team of chess experts and programmers manually altering the engineering between games – a fact that shouldn’t be lost on marketers leveraging real-time bidding (RTB) technologies. While RTB has transformed the online advertising space, it is a blunt instrument without the human touch.

Marketing is substantially more complex than chess, which is why machine-led tactics should be complemented by human-led strategy to produce the best results. Companies that input creative, pixels, rates and targets into a machine and allow it to execute a campaign programmatically do a disservice to the marketing discipline. Not only does this approach produce suboptimal results, but it removes the strategic control clients require. So how should machines and humans work together to achieve the best outcome?

Context

RTB systems that use machine learning benefit from significant data and predictive power, but gaps exist for the bidder in what we will call an outside context problem (OCP). Bidders try to predict conversion rates for different users, but the model encounters problems when variables outside its knowledge range substantially impact conversions.

For example, if a retail client offered a sale on products, the conversion rate would obviously rise as consumers made the most of the cheaper goods on offer. A bidder looking to hit a CPA strategy would push up the bids as a result, but this strategy may not be the correct one to adopt. The bidder also won’t know when the sale ends and the conversion rate drops, leaving the system – for a period of time – running much higher than the CPA target. This is an example of an outside context problem; in this case the variable driving performance is invisible to the bidder (the sale offer). Other context problems in RTB can include the weather, offline media or events, such as sports games for clients in the gambling sector.

When working with a bidder on such OCPs, humans can make manual alterations to the system to account for these factors. For example, a human may know that Saturday is the start of a five-day sale and, by examining the impact on conversion rate these sales typically had in the past, relevant data can be used to substantially improve the performance of the bidder.

Tactics And Strategy

There are a number of tactics and strategies that work well in programmatic, but each requires the human touch. Let’s take a look at some of them:

Retargeting. This is the most obvious.If you simply code a programmatic system to achieve the best CPA/ROI and provide the right pixels, the bidder will place most of its focus on retargeting. This is a flawed approach as the supposed ROI of retargeting is massively inflated even when attribution is applied. It should be people, not machines, who decide how much budget is focused on remarketing vs. prospecting, post-purchase/CRM or, indeed, branding.

Audience targeting. An RTB system left unregulated will always initially target the users it thinks are most likely to convert. This applies whether or not users are in the desired target audience. But not all sales are equal, even when revenue is accounted for. In pure direct-response (DR) campaigns this might not matter, but as programmatic moves beyond this, the target audience and desired outcomes against the target audience need to be taken into account, controlled by the planners and inputted into programmatic.

Creative control. Bidders do not understand creative messaging beyond a simple understanding about how different creative impacts conversion rates. Strategic control of the most relevant message to the most appropriate audience needs to be controlled by planners, even if elements of machine learning are used in the optimization. Some messaging may even be designed to produce lower conversion rates, but higher lifetime customer value.

Pacing of delivery. Pacing campaigns properly is a fundamental strategic system. For a machine, a sale is a sale, and any pound of ROI is equal to another, but most businesses do not operate like this. Flights and holidays, for example, have a strong availability problem and provide a prime example why activity should be paced across a campaign rather than front-loading the budget. By not doing so the airline could find itself with a glut of demand for two weeks of the month and empty seats when the budget runs out. While this is a simplistic example and unlikely to occur in real life, it highlights the importance of the human in mixing demand, budgets, opportunity cost and more.

As these examples show, human service, feedback and transparency as well as data and analytics skills are all vital in a world where humans and machines co-exist. Although machine learning in programmatic is here to stay, marketers should avoid leaving every decision to machines without human control.